Meta rule-based energy management strategy for battery/supercapacitor hybrid electric vehicles

Abstract

Driving pattern recognition (DPR) is widely used to improve the robustness of rule-based energy management strategies (EMS). However, the number and quality of preset patterns limit further performance improvements. This paper proposes a meta rule-based EMS to replace the role of DPR, wherein the meta rule refers to a rule that determines the parameters of the energy management rule. Firstly, the power distribution rule pattern is derived through the analysis of dynamic programming results. Then, the value of the parameters is obtained through clustering. Subsequently, a feature screening method based on mutual information is proposed to select useful features of driving conditions and system states. The seagull optimization algorithm is then employed to find the mathematical expression of the meta rule. Finally, simulation results demonstrate the effectiveness of the meta rule-based EMS. Compared with DPR-based and long short-term memory-based EMS, the meta rule-based EMS can reduce the Ampere-hour throughput of Li-ion batteries by 17.0% and 9.7% respectively under the China light-duty vehicle test cycle, and by 3.4% and 16.6% respectively under a real-world driving condition. The meta rule-based EMS requires only dozens of milliseconds for each moment and is suitable for real-time systems.